Method, Software Program, And System For Ranking Relative Risk Of A Plurality Of Transactions

ABSTRACT

A method for ranking relative risk of a plurality of transactions, wherein each of the transactions has associated therewith a plurality of variables. In one embodiment the method includes assigning a value to each of the variables associated with each of the transactions, aggregating the values assigned to each of the variables on a transaction by transaction basis to produce an aggregate risk level for each transaction, and ranking each of the transactions relative to one another based upon the aggregate risk level corresponding to each transaction. A corresponding software program and system are also disclosed.

FIELD OF THE INVENTION

The present invention relates to a method, software program, and systemfor ranking relative risk of a plurality of transactions. Moreparticularly, the present invention relates to a method, softwareprogram, and system for ranking relative operational risk of a pluralityof financial transactions.

By providing for the ranking of relative operational risk of a pluralityof financial transactions, the present invention provides a mechanismfor readily identifying “outlying” transaction risks (e.g., identifyingthe 10 riskiest transactions out of a group of 1000).

BACKGROUND OF THE INVENTION

Banks and other financial institutions typically attempt to identify andquantify risks associated with their business dealings. Two types ofrisks typically identified and quantified are credit risk and marketrisk. As their names imply, credit risk relates to risk associated withgiving or receiving credit and market risk relates to risk associatedwith changes in market conditions.

A third type of risk, which banks and other financial institutions arejust now beginning to address, is operational risk. One definition ofoperational risk promulgated by the Basel Committee on BankingSupervision (hereinafter “Basel Committee”) is that operational risk isa risk component other than credit or market risk and which is “the riskof direct or indirect loss resulting from inadequate or failed internalprocesses, people and systems or from external events”. Theaforementioned definition will be adopted for the purposes of thisapplication.

In any case, the Basel Committee proposes a number of approaches forallocating operating risk capital. In following the typical bankingmethodology of identifying and quantifying risk, these approachesinclude, but are not limited to, the Basic Indicator, the Standardizedapproach, the Internal Measurement approach, and the Loss Distributionapproach. However, none of these approaches appears to provide for theaggregation of individual risk factors of a plurality transactions on atransaction by transaction basis in order to identify the relative riskof each transaction. In other words, while the various approachesproposed by the Basel Committee attempt to identify and quantifyoperational risk, such approaches do not appear to provide a mechanismfor easily ranking the relative risk of a number of transactions withouttrying to explicitly quantify such risk (i.e., in terms of capitalloss).

Other risk analysis methodologies found in the financial area include,for example, the following:

U.S. Pat. No. 6,119,103, issued Sep. 12, 2000, to Basch et al. relatesto financial risk prediction systems and methods.

U.S. Pat. No. 5,978,778, issued Nov. 2, 1999, to O'Shaughnessy relatesto automated strategies for investment management.

U.S. Pat. No. 6,003,018, issued Dec. 14, 1999, to Michaud et al. relatesto a method for evaluating an existing or putative portfolio having aplurality of assets.

U.S. Pat. No. 5,812,987, issued Sep. 27, 1998, to Luskin et al. relatesto an invention for managing assets in one or more investment funds overa specified time.

U.S. Pat. No. 6,055,517, issued Apr. 25, 2000, to Friend et al. relatesto a method of simulating future cash flow for a given asset allocationunder a variety of economic conditions and measuring the frequency offailure of the cash flow to avoid one or more predefined risks.

U.S. Pat. No. 5,729,700, issued Mar. 17, 1998, to Melnikoff relates to aportfolio selector for selecting an investment portfolio from a libraryof assets based on investment risk and risk-adjusted return.

U.S. Pat. No. 5,884,287, issued Mar. 16, 1999, to Edesess relates to acomputer-implemented system and method to create an optimal investmentplan (given wealth goals stated in probabilistic form) and to displaythe resulting probability distributions of wealth accumulations atfuture times.

Further, various methods of risk or failure analysis have been proposedfor use in such fields as manufacturing, aviation, and disk drivemonitoring. These methodologies include, for example, the following:

U.S. Pat. No. 5,828,583, issued Oct. 27, 1998, to Bush et al. relates toa method for predicting an imminent failure of a disk drive.

U.S. Pat. No. 5,956,251, issued Sep. 21, 1999, to Atkinson et al.relates to a process of establishing valid statistical dimensionaltolerance limits for designs of detail parts that will enable accurateprediction of an economically acceptable degree of non-conformance of alarge flexible end item assembly.

Further still, in one type of inventory tracking methodology there ismaintained an A-B-C classification of items kept in store. Class A itemshave to be monitored very closely and should have some safety stock(because it is very costly to be out of stock of this class of item).Class B items are monitored less closely (because it is not as costly tobe out of stock of this class of item), and so on. Moreover, in one typeof scheduling methodology there is maintained a prioritization of jobsthat have to be done by the same resource or machine. Jobs are ranked,or prioritized, based on the dimensions of the workpiece and/or the timeit takes to do the job.

OBJECTS AND SUMMARY OF THE INVENTION

It is therefore an object of the present invention to provide a method,software program, and system for ranking relative risk of a plurality oftransactions.

It is therefore another object of the present invention to provide amethod, software program, and system for ranking relative risk of aplurality of transactions wherein “ranking of relative risk of aplurality of transactions” means ordering each transaction in comparisonto each of the other transactions (e.g. ordering may be from higher riskto lower risk or from lower risk to higher risk).

Another object of the present invention is to provide a method, softwareprogram, and system for ranking relative operational risk of a pluralityof financial transactions (e.g., financial trades in the equity,currency, debt, arbitrage, or fixed income markets).

Another object of the present invention is to provide a method, softwareprogram, and system for ranking relative operational risk of a pluralityof financial transactions without having to explicitly quantify aparticular risk in terms of risk capital.

Another object of the present invention is to provide a method, softwareprogram, and system for the aggregation of individual risk factors of aplurality of transactions on a transaction by transaction basis in orderto identify the relative risk of each transaction.

By providing a method, software program, and system for ranking relativeoperational risk of a plurality of financial transactions, the presentinvention provides a mechanism for readily identifying “outlying”transaction risks (e.g., identifying the 10 riskiest transactions out ofa group of 1000).

Such identification of “outlying” transaction risks is clearlyadvantageous for regulatory or internal auditing/accounting purposes.For example, an internal audit may examine the 10 riskiest transactionsout of a group of 1000 to ensure that each of the transactions is incompliance with all applicable rules and regulations.

In a specific embodiment, the present invention could be used to rank:i) the relative risk of all of the transactions made in a giventimeframe (e.g., within the last hour, within the last day, within thelast week, within the last year); or ii) the x number of riskiest tradesmade in a given timeframe (e.g., within the last hour, within the lastday, within the last week, within the last year), where x is an integer;or iii) the x number of least risky trades made in a given timeframe(e.g., within the last hour, within the last day, within the last week,within the last year), where x is an integer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a diagram of transaction stages and events according to anembodiment of the present invention;

FIG. 2A shows a diagram of various variables associated with the stagesand events of FIG. 1 according to an embodiment of the presentinvention;

FIG. 2B shows a diagram of definitions of various variables of FIG. 2Aaccording to an embodiment of the present invention;

FIG. 3 shows a flowchart identifying the steps carried out indetermining individual risk factor components according to an embodimentof the present invention;

FIG. 4 shows a risk factor curve according to an embodiment of thepresent invention;

FIG. 5 shows a flowchart identifying the steps carried out indetermining the relative risk of a transaction according to anembodiment of the present invention;

FIG. 6 shows a diagram of overall system flow according to an embodimentof the present invention;

FIG. 7 shows a diagram of a system architecture according to anembodiment of the present invention;

FIG. 8 shows a flowchart of a method according to an embodiment of thepresent invention;

FIG. 9 shows a flowchart of a method according to an embodiment of thepresent invention;

FIG. 10 shows a block diagram of a software program according to anembodiment of the present invention;

FIG. 11 shows a block diagram of a software program according to anembodiment of the present invention; and

FIG. 12 shows a block diagram of a system according to an embodiment ofthe present invention.

Among those benefits and improvements that have been disclosed, otherobjects and advantages of this invention will become apparent from thefollowing description taken in conjunction with the accompanyingfigures. The figures constitute a part of this specification and includeexemplary embodiments of the present invention and illustrate variousobjects and features thereof.

DETAILED DESCRIPTION OF THE INVENTION

As required, detailed embodiments of the present invention are disclosedherein; however, it is to be understood that the disclosed embodimentsare merely exemplary of the invention that may be embodied in variousforms. The figures are not necessarily to scale, some features may beexaggerated to show details of particular components. Therefore,specific structural and functional details disclosed herein are not tobe interpreted as limiting, but merely as a basis for the claims and asa representative basis for teaching one skilled in the art to variouslyemploy the present invention.

In one embodiment a method for ranking relative risk of a plurality oftransactions, wherein each of the transactions has associated therewitha plurality of variables is provided, including: assigning a value toeach of the variables associated with each of the transactions;aggregating the values assigned to each of the variables on atransaction by transaction basis to produce an aggregate risk level foreach transaction; and ranking each of the transactions relative to oneanother based upon the aggregate risk level corresponding to eachtransaction.

The step of assigning a value to each of the variables associated witheach of the transactions may further comprise assigning a normalizedrisk factor value to each of the variables associated with each of thetransactions based upon a raw value associated with each of thevariables of each of the transactions and the step of aggregating thevalues assigned to each of the variables on a transaction by transactionbasis to produce an aggregate risk level for each transaction mayfurther comprise aggregating the normalized risk factor values assignedto each of the variables on a transaction by transaction basis toproduce an aggregate risk level for each transaction.

Each value may be normalized to a predetermined normalization range. Thepredetermined normalization range may be between 0 to 1, inclusive.

Each variable may have associated therewith an operational tolerance andthe normalized risk factor value for each variable may be calculatedusing the formula:

RF=ξ·(e ^(x/β)−1)

where RF=the normalized risk factor value, ξ=0.5819767069,e=2.718182818, x=the raw value of the variable, and β=the operationaltolerance of the variable.

Each variable may be selected from the group of quantitative variablesand qualitative variables, wherein each variable which is a quantitativevariable may have associated therewith a raw value corresponding to anactual quantitative value, and wherein each variable which is aqualitative variable may have associated therewith a raw valuecorresponding to a value selected from a predetermined qualitative valuerange. The predetermined qualitative value range may be between 1 to 10,inclusive.

Each quantitative variable may be selected from the group including:elapsed time, historical volatility, deviation from average volatility,mark-to-market, trader error ratio, sales error ratio, frequency ofnotional, outgoing confirm delay/elapsed time, time to settlementcutoff, and fail recovery time.

Each qualitative variable may be selected from the group including:client sensitivity, execution method, client operating infrastructure,incoming confirm method, outgoing confirm method, internal creditrating, potential OD rates, payment instruction precedence, regulatoryrisk, master agreement (provisions for netting), country operatinginfrastructure, liquidity risk, template precedence, and productcomplexity.

The step of aggregating the normalized risk factor values assigned toeach of the variables on a transaction by transaction basis to producean aggregate risk level for each transaction may further compriseaggregating the normalized risk factor values using the formula:

${AR} = {\sum\limits_{j = 1}^{m}{w_{t}^{j} \cdot R_{t}^{j}}}$

where AR=the aggregate risk level, w_(t) ^(j) means the weights of the“j”th variable at time “t”, and R_(t) ^(j) means the normalized riskfactor value of the “j”th variable at time “t”.

The transactions may be ranked relative to one another in descendingorder of aggregate risk level. The transactions may be ranked relativeto one another in ascending order of aggregate risk level. The risk maybe operational risk.

In another embodiment a method for ranking relative risk of a pluralityof transactions, wherein each of the transactions has associatedtherewith a plurality of events and each of the events has associatedtherewith at least one variable is provided, including: assigning avalue to each of the variables associated with each of the transactions;aggregating the values assigned to each of the variables of each eventof each transaction to produce a by event aggregate risk level for eachevent of each transaction; aggregating the by event aggregate risklevels of each transaction to produce a by transaction aggregate risklevel for each transaction; and ranking each of the transactionsrelative to one another based upon the by transaction aggregate risklevel corresponding to each transaction.

The step of assigning a value to each of the variables associated witheach of the transactions may further comprise assigning a normalizedrisk factor value to each of the variables associated with each of thetransactions based upon a raw value associated with each of thevariables of each of the transactions and the step of aggregating thevalues assigned to each of the variables of each event of eachtransaction to produce a by event aggregate risk level for each event ofeach transaction may further comprise aggregating the normalized riskfactor values assigned to each of the variables of each event of eachtransaction to produce a by event aggregate risk level for each event ofeach transaction.

Each value may be normalized to a predetermined normalization range. Thepredetermined normalization range may be between 0 to 1, inclusive.

Each variable may have associated therewith an operational tolerance andthe normalized risk factor value for each variable may be calculatedusing the formula:

RF=ξ·(e ^(x/β)−1)

where RF=the normalized risk factor value, ξ=0.5819767069,e=2.718182818, x=the raw value of the variable, and β=the operationaltolerance of the variable.

The operational tolerance associated with a given variable of a givenevent may vary in dependence upon the given event of the transaction.

Each variable may be selected from the group of quantitative variablesand qualitative variables, wherein each variable which is a quantitativevariable may have associated therewith a raw value corresponding to anactual quantitative value, and wherein each variable which is aqualitative variable may have associated therewith a raw valuecorresponding to a value selected from a predetermined qualitative valuerange. The predetermined qualitative value range may be between 1 to 10,inclusive.

Each quantitative variable may be selected from the group including:elapsed time, historical volatility, deviation from average volatility,mark-to-market, trader error ratio, sales error ratio, frequency ofnotional, outgoing confirm delay/elapsed time, time to settlementcutoff; and fail recovery time.

Each qualitative variable may be selected from the group including:client sensitivity, execution method, client operating infrastructure,incoming confirm method, outgoing confirm method, internal creditrating, potential OD rates, payment instruction precedence, regulatoryrisk, master agreement (provisions for netting), country operatinginfrastructure, liquidity risk, template precedence, and productcomplexity.

The step of aggregating the normalized risk factor values assigned toeach of the variables of each event of each transaction to produce a byevent aggregate risk level for each event of each transaction mayfurther comprise aggregating the normalized risk factor values using theformula:

${E\; A\; R} = {\sum\limits_{j = 1}^{m}{w_{t}^{j;i} \cdot R_{t}^{j;i}}}$w _(t) ^(j;i)

where EAR=the by event aggregate risk level, means the weights of the“j”th variable on the “i”th event at time “t”, and R_(t) ^(j;i) meansthe normalized risk factor value of the “j”th variable on the “i”thevent at time “t” and the step of aggregating the by event aggregaterisk levels of each transaction to produce a by transaction aggregaterisk level for each transaction may further comprise aggregating thenormalized risk factor values and the by event aggregate risk levelsusing the formula:

${T\; A\; R} = {\sum\limits_{i = 1}^{n}{\sum\limits_{j = 1}^{m}{w_{t}^{j;i} \cdot R_{t}^{j;i}}}}$

where TAR=the transaction aggregate risk level, w_(t) ^(j;i) means theweights of the “j”th variable on the “i”th event at time “t”, and R_(t)^(j;i) means the normalized risk factor value of the “j”th variableon the “i”th event at time “t”.

The transactions may be ranked relative to one another in descendingorder of transaction aggregate risk level. The transactions may beranked relative to one another in ascending order of transactionaggregate risk level.

Each event of each transaction may be selected from the group including:a) order match; b) broker verification; c) financial confirmation; d)settlement confirmation; and e) terms confirmation.

The risk may be operational risk.

In another embodiment a software program for ranking relative risk of aplurality of transactions, wherein each of the transactions hasassociated therewith a plurality of variables is provided, including:means for assigning a value to each of the variables associated witheach of the transactions; means for aggregating the values assigned toeach of the variables on a transaction by transaction basis to producean aggregate risk level for each transaction; and means for ranking eachof the transactions relative to one another based upon the aggregaterisk level corresponding to each transaction.

The means for assigning a value to each of the variables associated witheach of the transactions may further comprise means for assigning anormalized risk factor value to each of the variables associated witheach of the transactions based upon a raw value associated with each ofthe variables of each of the transactions and the means for aggregatingthe values assigned to each of the variables on a transaction bytransaction basis to produce an aggregate risk level for eachtransaction may further comprise means for aggregating the normalizedrisk factor values assigned to each of the variables on a transaction bytransaction basis to produce an aggregate risk level for eachtransaction.

Each value may be normalized to a predetermined normalization range. Thepredetermined normalization range may be between 0 to 1, inclusive.

Each variable may have associated therewith an operational tolerance andthe normalized risk factor value for each variable may be calculatedusing the formula:

RF=ξ·(e ^(x/β)−1)

where RF=the normalized risk factor value, ξ=0.5819767069,e=2.718182818, x=the raw value of the variable, and β=the operationaltolerance of the variable.

Each variable may be selected from the group of quantitative variablesand qualitative variables, wherein each variable which is a quantitativevariable may have associated therewith a raw value corresponding to anactual quantitative value, and wherein each variable which is aqualitative variable may have associated therewith a raw valuecorresponding to a value selected from a predetermined qualitative valuerange. The predetermined qualitative value range may be between 1 to 10,inclusive.

Each quantitative variable may be selected from the group including:elapsed time, historical Volatility, deviation from average volatility,mark-to-market, trader error ratio, sales error ratio, frequency ofnotional, outgoing confirm delay/elapsed time, time to settlementcutoff, and fail recovery time.

Each qualitative variable may be selected from the group including:client sensitivity, execution method, client operating infrastructure,incoming confirm method, outgoing confirm method, internal creditrating, potential OD rates, payment instruction precedence, regulatoryrisk, master agreement (provisions for netting), country operatinginfrastructure, liquidity risk, template precedence, and productcomplexity.

The means for aggregating the normalized risk factor values assigned toeach of the variables on a transaction by transaction basis to producean aggregate risk level for each transaction may further comprise meansfor aggregating the normalized risk factor values using the formula:

${A\; R} = {\sum\limits_{j = 1}^{m}{w_{t}^{j} \cdot R_{t}^{j}}}$

where AR=the aggregate risk level, w_(t) ^(j) means the weights of the“j”th variable at time “t”, and R_(t) ^(j) means the normalized riskfactor value of the “j”th variable at time “t”.

The transactions may be ranked relative to one another in descendingorder of aggregate risk level. The transactions may be ranked relativeto one another in ascending order of aggregate risk level.

In one example, the risk may be operational risk.

In another embodiment a software program for ranking relative risk of aplurality of transactions, wherein each of the transactions hasassociated therewith a plurality of events and each of the events hasassociated therewith at least one variable is provided, including: meansfor assigning a value to each of the variables associated with each ofthe transactions; means for aggregating the values assigned to each ofthe variables of each event of each transaction to produce a by eventaggregate risk level for each event of each transaction; means foraggregating the by event aggregate risk levels of each transaction toproduce a by transaction aggregate risk level for each transaction; andmeans for ranking each of the transactions relative to one another basedupon the by transaction aggregate risk level corresponding to eachtransaction.

The means for assigning a value to each of the variables associated witheach of the transactions may further comprise means for assigning anormalized risk factor value to each of the variables associated witheach of the transactions based upon a raw value associated with each ofthe variables of each of the transactions and the means for aggregatingthe values assigned to each of the variables of each event of eachtransaction to produce a by event aggregate risk level for each event ofeach transaction may further comprise means for aggregating thenormalized risk factor values assigned to each of the variables of eachevent of each transaction to produce a by event aggregate risk level foreach event of each transaction.

Each value may be normalized to a predetermined normalization range. Thepredetermined normalization range may be between 0 to 1, inclusive.

Each variable may have associated therewith an operational tolerance andthe normalized risk factor value for each variable may be calculatedusing the formula:

RF=ξ·(e ^(x/β)−1)

where RF=the normalized risk factor value, ξ=0.5819767069,e=2.718182818, x=the raw value of the variable, and β=the operationaltolerance of the variable.

The operational tolerance associated with a given variable of a givenevent may vary in dependence upon the given event of the transaction.

Each variable may be selected from the group of quantitative variablesand qualitative variables, wherein each variable which is a quantitativevariable may have associated therewith a raw value corresponding to anactual quantitative value, and wherein each variable which is aqualitative variable may have associated therewith a raw valuecorresponding to a value selected from a predetermined qualitative valuerange. The predetermined qualitative value range may be between 1 to 10,inclusive.

Each quantitative variable may be selected from the group including:elapsed time, historical volatility, deviation from average volatility,mark-to-market, trader error ratio, sales error ratio, frequency ofnotional, outgoing confirm delay/elapsed time, time to settlementcutoff, and fail recovery time.

Each qualitative variable may be selected from the group including:client sensitivity, execution method, client operating infrastructure,incoming confirm method, outgoing confirm method, internal creditrating, potential OD rates, payment instruction precedence, regulatoryrisk, master agreement (provisions for netting), country operatinginfrastructure, liquidity risk, template precedence, and productcomplexity.

The means for aggregating the normalized risk factor values assigned toeach of the variables of each event of each transaction to produce a byevent aggregate risk level for each event of each transaction mayfurther comprise means for aggregating the normalized risk factor valuesusing the formula:

${E\; A\; R} = {\sum\limits_{j = 1}^{m}{w_{t}^{j;i} \cdot R_{t}^{j;i}}}$

where EAR=the by event aggregate risk level, w_(t) ^(j;i) means theweights of the “j”th variable on the “i”th event at time “t”, and R_(t)^(j;i) means the normalized risk factor value of the “j”th variable onthe“i”th event at time “t” and the means for aggregating the by eventaggregate risk levels of each transaction to produce a by transactionaggregate risk level for each transaction may further comprise means foraggregating the normalized risk factor values and the by event aggregaterisk levels using the formula:

${T\; A\; R} = {\sum\limits_{i = 1}^{n}{\sum\limits_{j = 1}^{m}{w_{t}^{j;i} \cdot R_{t}^{j;i}}}}$

where TAR=the transaction aggregate risk level, w_(t) ^(j;i) means theweights of the “j”th variable on the “i”th event at time “t”, and R_(t)^(j;i) means the normalized risk factor value of the “j”th variable onthe “i”th event at time “t”.

The transactions may be ranked relative to one another in descendingorder of transaction aggregate risk level. The transactions may beranked relative to one another in ascending order of transactionaggregate risk level.

Each event of each transaction may be selected from the group including:a) order match; b) broker verification; c) financial confirmation; d)settlement confirmation; and e) terms confirmation.

In one example, the risk may be operational risk.

In another embodiment a system for ranking relative risk of a pluralityof transactions, wherein each of the transactions has associatedtherewith a plurality of variables is provided, including: memory meansfor storing a software program; and processing means for processing thesoftware program; wherein the software program includes: means forassigning a value to each of the variables associated with each of thetransactions; means for aggregating the values assigned to each of thevariables on a transaction by transaction basis to produce an aggregaterisk level for each transaction; and means for ranking each of thetransactions relative to one another based upon the aggregate risk levelcorresponding to each transaction.

The means for assigning a value to each of the variables associated witheach of the transactions may further comprise means for assigning anormalized risk factor value to each of the variables associated witheach of the transactions based upon a raw value associated with each ofthe variables of each of the transactions and the means for aggregatingthe values assigned to each of the variables on a transaction bytransaction basis to produce an aggregate risk level for eachtransaction may further comprise means for aggregating the normalizedrisk factor values assigned to each of the variables on a transaction bytransaction basis to produce an aggregate risk level for eachtransaction.

Each value may be normalized to a predetermined normalization range. Thepredetermined normalization range may be between 0 to 1, inclusive.

Each variable may have associated therewith an operational tolerance andthe normalized risk factor value for each variable may be calculatedusing the formula:

RF=ξ·(e ^(x/β)−1)

where RF=the normalized risk factor value, ξ=0.5819767069,e=2.718182818, x=the raw value of the variable, and β=the operationaltolerance of the variable.

Each variable may be selected from the group of quantitative variablesand qualitative variables, wherein each variable which is a quantitativevariable may have associated therewith a raw value corresponding to anactual quantitative value, and wherein each variable which is aqualitative variable may have associated therewith a raw valuecorresponding to a value selected from a predetermined qualitative valuerange. The predetermined qualitative value range may be between 1 to 10,inclusive.

Each quantitative variable may be selected from the group including:elapsed time, historical volatility, deviation from average volatility,mark-to-market, trader error ratio, sales error ratio, frequency ofnotional, outgoing confirm delay/elapsed time, time to settlementcutoff, and fail recovery time.

Each qualitative variable may be selected from the group including:client sensitivity, execution method, client operating infrastructure,incoming confirm method, outgoing confirm method, internal creditrating, potential OD rates, payment instruction precedence, regulatoryrisk, master agreement (provisions for netting), country operatinginfrastructure, liquidity risk, template precedence, and productcomplexity.

The means for aggregating the normalized risk factor values assigned toeach of the variables on a transaction by transaction basis to producean aggregate risk level for each transaction may further comprise meansfor aggregating the normalized risk factor values using the formula:

${A\; R} = {\sum\limits_{j = 1}^{m}{w_{t}^{j} \cdot R_{t}^{j}}}$

where AR=the aggregate risk level, w_(t) ^(j) means the weights of the“j”th variable at time “t”, and R_(t) ^(j) means the normalized riskfactor value of the “j”th variable at time “t”.

The transactions may be ranked relative to one another in descendingorder of aggregate risk level. The transactions may be ranked relativeto one another in ascending order of aggregate risk level. The risk maybe operational risk.

In another embodiment a system for ranking relative risk of a pluralityof transactions, wherein each of the transactions has associatedtherewith a plurality of events and each of the events has associatedtherewith at least one variable is provided, including: memory means forstoring a software program; and processing means for processing thesoftware program; wherein the software program includes: means forassigning a value to each of the variables associated with each of thetransactions; means for aggregating the values assigned to each of thevariables of each event of each transaction to produce a by eventaggregate risk level for each event of each transaction; means foraggregating the by event aggregate risk levels of each transaction toproduce a by transaction aggregate risk level for each transaction; andmeans for ranking each of the transactions relative to one another basedupon the by transaction aggregate risk level corresponding to eachtransaction.

The means for assigning a value to each of the variables associated witheach of the transactions may further comprise means for assigning anormalized risk factor value to each of the variables associated witheach of the transactions based upon a raw value associated with each ofthe variables of each of the transactions and the means for aggregatingthe values assigned to each of the variables of each event of eachtransaction to produce a by event aggregate risk level for each event ofeach transaction may further comprise means for aggregating thenormalized risk factor values assigned to each of the variables of eachevent of each transaction to produce a by event aggregate risk level foreach event of each transaction.

Each value may be normalized to a predetermined normalization range. Thepredetermined normalization range may be between 0 to 1, inclusive.

Each variable may have associated therewith an operational tolerance andthe normalized risk factor value for each variable may be calculatedusing the formula:

RF=ξ·(e ^(x/β)−1)

where RF=the normalized risk factor value, β=0.5819767069,e=2.718182818, x=the raw value of the variable, and β=the operationaltolerance of the variable.

The operational tolerance associated with a given variable of a givenevent may vary in dependence upon the given event of the transaction.

Each variable may be selected from the group of quantitative variablesand qualitative variables, wherein each variable which is a quantitativevariable may have associated therewith a raw value corresponding to anactual quantitative value, and wherein each variable which is aqualitative variable may have associated therewith a raw valuecorresponding to a value selected from a predetermined qualitative valuerange. The predetermined qualitative value range may be between 1 to 10,inclusive.

Each quantitative variable may be selected from the group including:elapsed time, historical volatility, deviation from average volatility,mark-to-market, trader error ratio, sales error ratio, frequency ofnotional, outgoing confirm delay/elapsed time, time to settlementcutoff, and fail recovery time.

Each qualitative variable may be selected from the group including:client sensitivity, execution method, client operating infrastructure,incoming confirm method, outgoing confirm method, internal creditrating, potential OD rates, payment instruction precedence, regulatoryrisk, master agreement (provisions for netting), country operatinginfrastructure, liquidity risk, template precedence, and productcomplexity.

The means for aggregating the normalized risk factor values assigned toeach of the variables of each event of each transaction to produce a byevent aggregate risk level for each event of each transaction mayfurther comprise means for aggregating the normalized risk factor valuesusing the formula:

${E\; A\; R} = {\sum\limits_{j = 1}^{m}{w_{t}^{j;i} \cdot R_{t}^{j;i}}}$

where EAR=the by event aggregate risk level, means the weights of the“j”th variable on the

w _(t) ^(j;i)

“i”th event at time “t”, and R_(t) ^(j;i) means the normalized riskfactor value of the “j”th variable on the“i”th event at time “t” and the means for aggregating the by eventaggregate risk levels of each transaction to produce a by transactionaggregate risk level for each transaction may further comprise means foraggregating the normalized risk factor values and the by event aggregaterisk levels using the formula:

${T\; A\; R} = {\sum\limits_{i = 1}^{n}{\sum\limits_{j = 1}^{m}{w_{t}^{j;i} \cdot R_{t}^{j;i}}}}$

where TAR=the transaction aggregate risk level, w_(t) ^(j;t) means theweights of the “j”th variable on the “i”th event at time “t”, and R_(t)^(j;i) means the normalized risk factor value of the “j”th variable onthe “i”th event at time “t”.

The transactions may be ranked relative to one another in descendingorder of transaction aggregate risk level. The transactions may beranked relative to one another in ascending order of transactionaggregate risk level.

Each event of each transaction may be selected from the group including:a) order match; b) broker verification; c) financial confirmation; d)settlement confirmation; and e) terms confirmation.

In one example, the risk may be operational risk.

While the present invention may be used to rank the risk of differenttypes of transactions (e.g., financial trades in the equity, currency,debt, arbitrage, or fixed income markets), an example embodiment appliedto currency trades will now be described (but is not meant to limit thepresent invention).

In this regard, and referring now to FIG. 1, a diagram showing thestages and events that a trade goes through according to this embodimentof the present invention is depicted. In this FIG. 1 “events” aremilestones that occur (e.g., Order Match, Broker Verification, andFinancial Confirm) and “stages” are markers delineating various events.As well known to those of ordinary skill in the art, other currencytrades and/or other types of trades may go through different stages andevents and/or may go through these same stages and events in a differentorder. For example, while Order Match and Broker Verification are shownas parallel events of stage 1 in this embodiment, other embodimentscould have the two events occurring serially. Further, while stages 3and 4 are shown as occurring parallel to stage 5, other configurationsare possible.

Moreover, it is noted that in this embodiment a trade may require eitheran Order Match (i.e., the trade is a client trade) or a BrokerVerification (i.e., the trade is a bank trade), but not both. In anotherembodiment the two events may be non-mutually exclusive. Further, inthis embodiment the Terms Confirm event of stage 5 is done in parallelwith the events of stages 3 and 4. In another embodiment the TermsConfirm event of stage 5 and the events of stages 3 and 4 may occurserially.

In any case, as seen in this FIG. 1, a currency trade according to thepresent example goes through 5 stages, each delineating one or moreevents. The specific variables associated with each of the events ofthis embodiment are shown in more detail in FIG. 2A. It is noted thatthe variables identified in FIG. 2A are used for the purpose of exampleonly, and that as well known to those of ordinary skill in the art,other currency trades and/or other types of trades may associatedifferent, and/or fewer, and/or more variables to each of the events. Inany case, a number of the variables identified in FIG. 2A are definedmore specifically in FIG. 2B.

Regarding the timing between the events of the currency trade exampledepicted in FIG. 1, it is noted that the Order Match, BrokerVerification and Financial Confirm events may occur relatively quickly(e.g., on the order of hours), with the Settlement Confirm, Value Date,and Terms Confirm events taking a relatively longer period of time(e.g., on the order of days). It is noted that the above-describedtiming is used for the purpose of example only, and that as well knownto those of ordinary skill in the art, other currency trades and/orother types of trades may utilize different timing between each of thestages and events (e.g. earlier events occurring on the order of minutesor later events occurring on the order of weeks). Regardless, the riskof a trade may be a function of time as well as a function of what stageor event the trade is in. In other words, a trade may be open for manymonths, for example, and its relative risk ranking compared to othertrades may change during that time.

Such relative risk may be obtained via an aggregation function thataggregates the risk level of a number of individual risk components. Theaggregation function may yield a single value that represents therelative risk of a given trade versus the relative risk of one or moreother trades. The risk levels of the individual risk components thatform the input to the aggregation function may be dependent orindependent of one another.

Still referring to FIG. 1, it is noted that as a transaction movesthrough the various stages and events the transaction may be subject tovarious operational checks/processes to help ensure completeness andaccuracy. It is further noted that in the present example (which ispresented for illustration only, and is not intended to be restrictive),on the order of 5,000-8,000 new trades may come in to stage 1 on a givenday and on the order of 100,000 open trades may exist in the laterstages on a given day.

Referring now to FIG. 3, a flowchart showing how the risk levels of theindividual risk components are determined in the present exampleembodiment is depicted. At Step 301 measures of certain attributes(i.e., input variables) are obtained. The values of the input variablesmay be obtained from a database or from another source. The inputvariables may be updated at certain times. Each input variable may bequantitative (e.g., a measure of time) or qualitative (e.g., a measureof client sensitivity). Quantitative variables may be assigned theiractual quantitative values (e.g., a time value or a dollar value).Qualitative variables may be ranked on a qualitative variables scale(e.g. a scale from 1 to 10). Each input variable may have assignedtherewith a maximum operational tolerance level (e.g., indicative ofmaximum risk). The maximum tolerance level for a quantitative variablemay be a defined control standard (e.g., unconfirmed trades maximumtolerance level=trade time+x or outgoing confirm delay maximum tolerancelevel=trade time+y, wherein x and y are appropriate time units). Themaximum tolerance level for a qualitative variable may be the top valueof the qualitative variable scale (e.g., 10). The maximum tolerancelevel for a given variable may vary over time and/or may vary for one ormore events of the trade. Each input variable may be dependent upon oneor more other input variables, and/or may be independent of one or moreother input variables, and/or may be random. Each input variable may bea factor in one or more events. Each input variable may be risk weighteddifferently within one or more events. Each input variable may be fixed(i.e., does not change over time), or may fluctuate essentiallycontinuously (e.g., deviation from historical volatility), or may changeonly when a trade has reached a new event.

Still referring to FIG. 3, a normalized risk factor value (e.g., between0 and 1, inclusive) for each variable is determined at Step 303 usingthe following formula:

RF=ξ·(e ^(x/β)−1)

where RF=the normalized risk factor value, ξ=0.5819767069,e=2.718182818, x=the raw value of the variable, and β=the operationaltolerance of the variable.

The operational tolerance associated with a given variable of a givenevent may vary in dependence upon the given event of the transaction. Inthis embodiment of the present invention, elapsed time is a variable in4 events and operational tolerance (i.e., β) differs in each event asfollows: Order Match β=3 hours; Broker Verification β=24 hours (i.e.,event start time+1 day); Financial Confirmation β=24 hours (i.e., eventstart time+1 day); and Terms Confirmation β=240 hours (i.e., event starttime+10 days). Below is Table 1 showing normalized risk factor valuescorresponding to the elapsed time variable for each of the four eventsdiscussed above:

TABLE 1 Broker Financial Terms Order Match Verification ConfirmationConfirmation (Stage 1) (Stage 2) (Stage 3) (Stage 5) Time (x) β = 3hours β = 24 hours β = 24 hours β = 240 hours 1 hour 0.23024 0.024760.02476 0.00243 2 hours 0.55156 0.05058 0.05058 0.00487 3 hours 1.000000.07749 0.07749 0.00732 4 hours 1.00000 0.10555 0.10555 0.00978 10 hours1.00000 0.30083 0.30083 0.02476 20 hours 1.00000 0.75714 0.75714 0.05058110 hours 1.00000 1.00000 1.00000 0.33839

Finally, as discussed above, the individual risk factor values for eachtransaction are aggregated to produce a relative risk levelcorresponding to a particular trade. More particularly, a by eventaggregate risk level for each event of each transaction may becalculated using the formula:

${E\; A\; R} = {\sum\limits_{j = 1}^{m}{w_{t}^{j;i} \cdot R_{t}^{j;t}}}$

where EAR=the by event aggregate risk level, w_(t) ^(j;i) means theweights of the “j”th variable on the “i”th event at time “t”, and R_(t)^(j;i) means the normalized risk factor value of the “j”th variable onthe “i”th event at time “t” and the by transaction aggregate risk levelmay be calculated using the formula:

${T\; A\; R} = {\sum\limits_{i = 1}^{n}{\sum\limits_{j = 1}^{m}{w_{t}^{j;i} \cdot R_{t}^{j;i}}}}$

where TAR=the transaction aggregate risk level, w_(t) ^(j;i) means theweights of the “j”th variable on the “i”th event at time “t”, and R_(t)^(j;i) means the normalized risk factor value of the “j”th variableon the “i”th event at time “t”.

Referring now to FIG. 4, a risk factor curve corresponding to theformula:

RF=ξ·(e ^(x/β)−1)

is depicted. As seen in this figure, risk accelerates as the variable'svalue approaches the operational tolerance.

Referring now to FIG. 5, which shows an overall relative risk levelcalculation process according to this embodiment of the presentinvention, it is seen that various input variables are each applied fromDatabase 501 (which may be a single database as depicted or multipledatabases) to Risk Factor Function 503 (which may be a single functionas depicted or multiple functions) and the output from the Risk FactorFunction is applied to Aggregation Function 505 (which may be a singlefunction as depicted or multiple functions) to generate a relative risklevel for each trade. In addition, expert input may be applied to theRisk Factor Function 503 and/or the Aggregation Function 505 to at leastpartially control the output of the functions. It is noted that whilethis FIG. 5 shows three representative input variables, any desiredinput variables may of course be used.

Referring now to FIG. 6, which shows overall system flow in anotherexample embodiment of the present invention, it is seen that TradingData 601 and Sales Data 603 are sent to Operations System 605 to beforwarded to Risk Prioritization System 607 as Transaction Activity. TheTrading Data 601, the Sales Data 603, and the Transaction Activity maybe communicated in real-time and/or historically. In any case, RiskSnapshots 609 are periodically posted for the use of Operations System605. In addition, Operations System 605 may poll Risk PrioritizationSystem 607 for Risk Snapshots 609 when desired.

Referring now to FIG. 7, which shows a system architecture in anotherexample embodiment of the present invention, it is seen that Database701 includes Skeleton 701 a, Transactions 701 b, Reports 701 c, andExceptions 701 d. Further, Database 701 is acted upon by MaintenanceUtility 1703, Transaction Feed 705, Calculation Engine 707, NotificationManager 709, and Display Server 711. Moreover, Management Viewer 713 (aswell as any other desired Front End System 715) interact with DisplayServer 711 to provide views into the data.

In yet another embodiment of the present invention, the raw valuesassociated with each of the variables of a transaction, and/or thenormalized risk factor values associated with each of the variables of atransaction may be identified in connection with selected one(s) of thetransaction(s) ranked by the present invention. The identification maybe made in the form of a “drill-down” process by which more and moredetail is progressively identified to a user. Such identification of theraw values associated with each of the variables of a transaction and/orthe normalized risk factor values associated with each of the variablesof a transaction may be used to aid in identifying the underlyingreasons behind a given relative risk level (i.e. “What is making thetransaction so risky?”).

More particularly, in this example, after the present invention hasdetermined what are the ten most risky trades, the user may desire toknow why a particular trade is so risky. A mechanism could be providedto allow the user to “drill-down” and open a window for a specific tradeand look at the particular values of each one of the various riskfactors and/or raw variables for that trade. Thus, the user would beable to get a better understanding of why a particular trade is risky.

In yet another embodiment of the present invention, a feedback loop maybe employed wherein historical data corresponding to prior transactionrankings, and/or prior raw values associated with given variables,and/or prior normalized risk factor values associated with givenvariables are used at least partially in determining the ranking of newtransactions. In addition, the feedback loop may use (at leastpartially) input from a user concerning the appropriateness of one ormore prior transaction rankings in determining the ranking of newtransactions.

Referring now to FIG. 8, a flowchart showing a method for rankingrelative risk of a plurality of transactions according to anotherembodiment of the present invention is shown.

More particularly, it is seen that at Step 801 a value is assigned toeach of the variables associated with each of the transactions. At Step803 the values assigned to each of the variables are aggregated on atransaction by transaction basis to produce an aggregate risk level foreach transaction. At Step 805 each of the transactions is rankedrelative to one another based upon the aggregate risk levelcorresponding to each transaction.

Referring now to FIG. 9, a flowchart showing a method for rankingrelative risk of a plurality of transactions according to anotherembodiment of the present invention is shown.

More particularly, it is seen that at Step 901 a value is assigned toeach of the variables associated with each of the transactions. At Step903 the values assigned to each of the variables of each event of eachtransaction are aggregated to produce a by event aggregate risk levelfor each event of each transaction. At Step 905 the by event aggregaterisk levels of each transaction are aggregated to produce a bytransaction aggregate risk level for each transaction. At Step 907 eachof the transactions are ranked relative to one another based upon the bytransaction aggregate risk level corresponding to each transaction.

Referring now to FIG. 10, a block diagram of a software program forranking relative risk of a plurality of transactions according toanother embodiment of the present invention is shown. As seen in thisFig., Software Program 1001 includes:

-   -   1) Assignment Module 1003 for assigning a value to each of the        variables associated with each of the transactions;    -   2) Aggregating Module 1005 for aggregating the values assigned        to each of the variables on a transaction by transaction basis        to produce an aggregate risk level for each transaction; and    -   3) Ranking Module 1007 for ranking each of the transactions        relative to one another based upon the aggregate risk level        corresponding to each transaction.

Assignment Module 1003 may include a method for assigning a normalizedrisk factor value to each of the variables associated with each of thetransactions based upon a raw value associated with each of thevariables of each of the transactions and the Aggregating Module 1005may include means for aggregating the normalized risk factor valuesassigned to each of the variables on a transaction by transaction basisto produce an aggregate risk level for each transaction.

Referring now to FIG. 11, a block diagram of a software program forranking relative risk of a plurality of transactions according toanother embodiment of the present invention is shown. As seen in thisFig., Software Program 1101 includes:

-   -   1) Assignment Module 1103 for assigning a value to each of the        variables associated with each of the transactions;    -   2) First Aggregating Module 1105 for aggregating the values        assigned to each of the variables of each event of each        transaction to produce a by event aggregate risk level for each        event of each transaction;    -   3) Second Aggregating Module 1107 for aggregating the by event        aggregate risk levels of each transaction to produce a by        transaction aggregate risk level for each transaction; and    -   4) Ranking Module 1109 for ranking each of the transactions        relative to one another based upon the by transaction aggregate        risk level corresponding to each transaction.

Assignment Module 1103 may include a method for assigning a normalizedrisk factor value to each of the variables associated with each of thetransactions based upon a raw value associated with each of thevariables of each of the transactions and the First Aggregating Module1105 may include a method for aggregating the normalized risk factorvalues assigned to each of the variables of each event of eachtransaction to produce a by event aggregate risk level for each event ofeach transaction.

Referring now to FIG. 12, a block diagram of a system according toanother embodiment of the present invention is shown. As seen in thisFig., Computer 1201 includes Memory 1203 for storing a software program(not shown) and CPU 1205 for processing the software program. Monitor1207, Keyboard 1209, Mouse 1211, and Printer 1213 are connected toComputer 1201 to provide user input/output. Input/output to the softwareprogram may also be accomplished via a storage medium (e.g., a harddrive or a CD) and/or a network, each of which is not shown The softwareprogram stored in Memory 1203 and processed by CPU 1205 may of course beone of the software programs of the present invention. In any case, thedetails of each of Computer 1201, Memory 1203, CPU 1205, Monitor 1207,Keyboard 1209, Mouse 1211, and Printer 1213 are well known to those ofordinary skill in the art and will not be discussed further.

While a number of embodiments of the present invention have beendescribed, it is understood that these embodiments are illustrativeonly, and not restrictive, and that many modifications may becomeapparent to those of ordinary skill in the art. For example, the presentinvention may be applied to any tradable product (e.g., equity,currency, debt, arbitrage, or fixed income). Further, while a number ofvariables useful in ranking risk according to the present invention havebeen described, any other appropriate variables may of course be used(e.g. Herstaat Risk). More particularly, other exogenous variables(i.e., relating to market conditions) or endogenous variables (i.e.,relating to internal conditions) may of course be used. Further still,while the aggregation element(s) of the present invention have beendescribed principally as employing summation and multiplication, othermathematical operator(s) and/or function(s) may of course be used (e.g.,hybrids or mixtures of summations and products). Further still, theformula (s) used for aggregation may be static or dynamic. If dynamic,they may change periodically (e.g., every second, every hour, every day,every week), at certain times of the day, at certain times of the week,at certain times of the year, when directed by a user, or when one ormore conditions is met. Such conditions could relate to one or morevariable raw values and/or one or more normalized risk factor values. Ifthe aggregation formula (s) are dynamic, the weights and/or mathematicaloperator(s) and/or function(s) may change. Further still, while thegeneration of the normalized risk factor values has been describedprincipally with regard to a single given formula producing a singlegiven exponential curve, other appropriate formula (s) producing otherappropriate curve(s), such as other appropriate exponential curves(s)for example, may of course be used. Other formulas (or functions) mayinclude the linear function, the quadratic function, or any other poweror polynomial function, for example. Further still, one or more look-uptables may be used to generate the normalized risk factor values. Thelook-up table(s) may be associated with one or more variables (e.g.,client sensitivity, country infrastructure, product complexity, and soon). The look-up table(s) may be used on their own or in combinationwith one or more formulas to generate the normalized risk factor values.Further still, the formula (s) and/or look-up table(s) may be static ordynamic. If dynamic, they may change periodically (e.g., every second,every hour, every day, every week), at certain times of the day, atcertain times of the week, at certain times of the year, when directedby a user, or when one or more conditions is met. Such conditions couldrelate to one or more variable raw values and/or one or more normalizedrisk factor values. Further still, the present invention could be usedto rank: i) the relative risk of all of the transactions made in a giventimeframe (e.g., within the last hour, within the last day, within thelast week, within the last year); or ii) the x number of riskiest tradesmade in a given timeframe (e.g., within the last hour, within the lastday, within the last week, within the last year), where x is an integer;or iii) the x number of least risky trades made in a given timeframe(e.g., within the last hour, within the last day, within the last week,within the last year), where x is an integer. Further still, the presentinvention may be used as a regulatory tool and/or for internal auditingor accounting. Further still, the present invention may update dataincluding raw variable values and/or normalized risk factor valuesperiodically (e.g., every second, every hour, every day, every week), atcertain times of the day, at certain times of the week, at certain timesof the year, when directed by a user, or when one or more conditions ismet. Such conditions could relate to one or more variable raw valuesand/or one or more normalized risk factor values. Further still, thetransactions ranked by the present invention may be filtered (e.g., byevent, by client, or by currency). Further still, the transactionsranked by the present invention may be completed transactions and/or“open” transactions which are being processed. Further still, one ormore risk factors may be dynamic and based on a learning mechanism. Forexample, suppose that the present invention is used to keep track of thenumber of amendments that a particular trader does on each one of histrades (which, by the way, may also depend on the trade complexity). Ifthe number of the trader's amendments is going down over time (i.e., thetrader is getting more experience), then the value of his risk factor isgoing down. Therefore, in this example the present invention may keeptrack of statistics pertaining to each individual operator or trader andmay dynamically change (based on experience) the values of theappropriate risk factor(s). Of course, any appropriate risk factor(and/or corresponding variable(s)) may be dynamically changed based onsuch a learning mechanism. Further still, the present invention may beapplied to prioritizations in other settings as well (e.g., themaintenance of airplanes by an airline). For example, the presentinvention may be used to track which airplane(s) should be overhauledfirst (e.g., the airplanes could be prioritized based on theprobabilities that something can go wrong with the airplane as a wholeand/or the probabilities that something can go wrong with one or moreparts of the airplane). Further still, the memory of the system maycomprise a magnetic hard drive, a magnetic floppy disk, a compact disk,a ROM, a RAM, and/or any other appropriate memory. Further still, thecomputer of the system may comprise a stand-alone PC-type micro-computeras depicted or the computer may comprise one of a mainframe computer ora mini-computer, for example. Further still, another computer couldcommunicate with the software program and/or computer of the system byutilizing a local area network, a wide area network, or the Internet,for example.

1. A method for ranking relative risk of a plurality of transactions,wherein each of the transactions has associated therewith a plurality ofvariables, comprising: assigning a value to each of the variablesassociated with each of the transactions; aggregating the valuesassigned to each of the variables on a transaction by transaction basisto produce an aggregate risk level for each transaction; and rankingeach of the transactions relative to one another based upon theaggregate risk level corresponding to each transaction. 2.-84.(canceled)
 85. The method of claim 1, wherein the determined values forat least some of the plurality of variables are normalized.
 86. Themethod of claim 85, further comprising providing a risk factor reportusing the transaction rankings.
 87. The method of claim 86, wherein therisk factor report includes the determined values for at least some ofthe plurality of variables.
 88. The method of claim 86, wherein the riskfactor report includes the normalized values for at least some of theplurality of variables.
 89. The method of claim 1, further comprisingfiltering transactions to be ranked using at least one of: an event, aclient, a currency.
 90. A processor-implemented method for rankingrelative risk of a plurality of transactions comprising: obtaining via aprocessor a plurality of events associated with each of a plurality oftransactions; determining via the processor a value for each of aplurality of variables associated with each of the plurality of events;calculating via the processor an event risk level for each of theplurality of events by aggregating the determined values of theplurality of variables on a by event basis; calculating via theprocessor a transaction risk level for each of the plurality oftransactions by aggregating the calculated event risk levels for theplurality of events on a by transaction basis; and ranking via theprocessor each of the transactions relative to one another using thecalculated transaction risk level corresponding to each transaction. 91.The method of claim 90, wherein the determined values for at least someof the plurality of variables are normalized.
 92. The method of claim91, further comprising providing a risk factor report using thetransaction rankings.
 93. The method of claim 92, wherein the riskfactor report includes the determined values for at least some of theplurality of variables.
 94. The method of claim 92, wherein the riskfactor report includes the normalized values for at least some of theplurality of variables.
 95. The method of claim 90, further comprisingfiltering transactions to be ranked using at least one of: an event, aclient, a currency.